PhD Scientific Days 2022

Translational Medicine I. (Poster discussion will take place on the terrace of the room during the Coffee Break)

Automated Analysis of Planimetric Infarct Size Measurement by Artificial Intelligence

Előadó neve

Csenger, Kovácsházi

Előadó munkahelye

Semmelweis University, Department of Pharmacology and Pharmacotherapy

Előadó telefonszáma

+36209987894

Előadó e-mail címe

kovacshazi.csenger@med.semmelweis-univ.hu

Az előadás címe

Automated Analysis of Planimetric Infarct Size Measurement by Artificial Intelligence

Szerző(k) neve és munkahelye

AUTHORS
Csenger Kovácsházi1, András Horváth2, Csanád Tabajdi2, Adrienn Rácz2, Gábor B. Brenner1, Tamás G. Gergely1, Bence Ágg1,3,4, Péter Ferdinandy1,3,4, Anikó Görbe1,3,4, Zoltán Giricz1,3

AFFILIATIONS
1. Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
2. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary
3. Pharmahungary Group, 6722 Szeged, Hungary
4. MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary

Bemutatás módja

Szóbeli

Szekció

Translational Medicine I. (Poster discussion will take place on the terrace of the room during the Coffee Break)

Language of the presentation

English

Preferred session

Health Sciences

Összefoglaló szövege

Introduction: Planimetric measurement of myocardial infarct size (IS) by Evans blue and 2,3,5-triphenyltetrazolium (TTC) staining is widely used in preclinical studies. However, manual analysis of the images takes considerable amount of time and results of the evaluation may vary between analyzers. Artificial intelligence (AI) is widely used for image segmentation, but was not applied for the analysis of such images before.
Aims: We aimed to develop an AI-based, automatic IS analysis software to reduce image analysis time and increase analysis reproducibility.
Methods: Images were acquired from experiments on male Wistar rats with 30 minutes occlusion and 120 minutes reperfusion of the left anterior descending coronary artery. Area-at-risk (AAR) was negatively stained with Evans Blue, and IS was negatively stained with TTC. A U-NET architecture-inspired neural network with five downscaling and five upscaling layers was implemented and trained using cross-entropy loss and Adam optimizer. AI was trained on 1200 randomly selected slices and then results were compared with human expert analyzer.
Results: Trained AI labelled the myocardium optimally and the analysis error on IS/AAR was comparable to the variance of the manual analysis. When slices were pre-annotated with the AI and then a human analyzer applied adjustments, changes were moderate in IS/AAR values of the animals (9.41±8.27 %; mean±SD), however, the mean and SEM of the individual experimental groups did not significantly differ.
Conclusion: Since the error of our AI software was comparable to the variance between human analyzers, and the modifications deemed necessary by human analyzer were moderate after pre-annotation of images, our AI software can greatly reduce measurement time and may increase data reproducibility. However, it may not be applicable as an automated, human-free analysis software in its current version.
Funding: Project was supported by Thematic Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Bioimaging thematic programmes of the Semmelweis University, NKFIH of Hungary K139237 to AG. C.K. was supported by NTP-NFTÖ-21-B-0300 and SE250+ Excellence Scholarship (EFOP-3.6.3-VEKOP-16-2017-00009), NRDI Fund (2019-1.1.1-PIACI-KFI-2019-00367)

University and Doctoral School

Semmelweis University, Doctoral School of Pharmaceutical Sciences

Supervisor

Dr. Zoltán Giricz

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

5976

Start

10:45

End

11:00

Authors (legacy)

AUTHORS
Csenger Kovácsházi1, András Horváth2, Csanád Tabajdi2, Adrienn Rácz2, Gábor B. Brenner1, Tamás G. Gergely1, Bence Ágg1,3,4, Péter Ferdinandy1,3,4, Anikó Görbe1,3,4, Zoltán Giricz1,3

AFFILIATIONS
1. Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary
2. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary
3. Pharmahungary Group, 6722 Szeged, Hungary
4. MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, 1089 Budapest, Hungary